A personalized recommender system for SaaS services

Summary In this paper, we propose the Software‐as‐a‐Service (SaaS) Recommender (SaaSRec), a personalized reputation‐based QoS‐aware recommender system (RS) for SaaS services. SaaSRec semantically processes user requests in order to find business‐oriented matching services, which are then filtered to...

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Bibliographic Details
Published in:Concurrency and computation 2017-02, Vol.29 (4), p.np-n/a
Main Authors: Afify, Yasmine M., Moawad, Ibrahim F., Badr, Nagwa L., Tolba, Mohamed F.
Format: Article
Language:English
Subjects:
Acceptance
cloud services
Cold starts
Content analysis
Feedback
Filtering
Filtration
hybrid filtering
justification
Personalized
personalized recommendation
QoS
Recall
Recommendations
Recommender systems
reputation
Reputations
Software services
Software-as-a-service
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Summary:Summary In this paper, we propose the Software‐as‐a‐Service (SaaS) Recommender (SaaSRec), a personalized reputation‐based QoS‐aware recommender system (RS) for SaaS services. SaaSRec semantically processes user requests in order to find business‐oriented matching services, which are then filtered to satisfy the user QoS requirements and service characteristics. Subsequently, hybrid filtering is utilized to validate the services set on the basis of services metadata, reputation and user interests. Finally, the recommended set of services is ranked using a unique combination of factors: Relevance to user profile, service reputations and service cost. Moreover, we propose a new method for calculating the service reputation from the objective time‐weighted user feedbacks. SaaSRec addresses many challenges faced by the generic RSs: User cold‐start problem, limited content analysis and low performance. In respect of service RSs, SaaSRec tackles the disregarding of relevant factors to services recommendation: Cloud service characteristics, user physical location, service reputation and user interests. Moreover, SaaSRec provides a hybrid justification for the recommended services to increase the user's acceptance. Experimental evaluation against a real‐world services dataset has been carried out, and the results show that the proposed recommendation approach surpasses other collaborative filtering‐based recommendation approaches in respect of both precision and recall. This performance improvement was verified using different matrix density levels and number of recommendations. Copyright © 2016 John Wiley & Sons, Ltd.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.3877